Data Scientist/Senior Data Scientist

Science and Technology Facilities Council

Salary range: Band D is £31,931 - £34,709 and for Band E is £39,748 - £44,166  plus a recruitment and retention allowance of up to £4,000 per annum

Contract Type: Permanent

Hours: Full time/Part Time (minimum of 25 hours a week)

Location: STFC Rutherford Appleton Laboratory, Harwell, Oxfordshire

Closing date: 9th October 2022

https://www.careersportal.co.uk/UKRI-careers/jobs/data-scientist-senior-data-scientist-2436

About Us

At the Science and Technology Facilities Council (STFC), one of Europe’s largest multidisciplinary research organisations, the expertise of our computing staff is the key to making differences in research. STFC’s Scientific Computing Department (SCD) develops leading-edge software, computer, and data storage infrastructures, to support the work of world-class science both within STFC and internationally.  We work with the very latest technologies to advance science, particularly focussing on applied research. Whether it is the search for the Higgs Boson and dark matter, analysing climate data, or genomics, we address a number of biggest and most challenging problems in scientific computing. 

As part of UK Research and Innovation, STFC offers a working environment and benefits package designed to provide an excellent work/life balance. For this opportunity, we welcome applications on a full-time, part-time (minimum 25 hours), or term-time only basis and we also offer a flexible working scheme. Further benefits include 30 days’ (pro-rata) annual leave, 10.5 public and privilege days, Christmas shut down, a workplace nursery, an exceptionally defined benefit pension scheme, and social and sporting activities and societies. STFC is an open and inclusive work environment, committed to promoting equality, diversity, and inclusion. We are committed to developing our staff, and training will be provided in relevant areas. 

Background

The Harwell campus of the Science and Technologies Facilities Council is home to the Rutherford Appleton Laboratory (RAL) and to the UK research community’s large-scale experimental Facilities. These include the Diamond Synchrotron and Electron Microscopy facilities, the ISIS Neutron and Muon Facility, the Central Laser Facility, and the Centre for Environmental Data Analysis (CEDA). Researchers from universities and from industry use these facilities for a very wide range of scientific applications ranging from revealing ancient fossils and improving battery technology, to characterising materials to understand the impact of climate change.

The Scientific Machine Learning (SciML) Group, situated within the Scientific Computing Department in RAL, works very closely with these large-scale experimental facilities, and their users, in applying and developing state-of-the-art AI and machine learning methods to translate their data into innovative science. The Group is also a ‘Turing Hub’ – a component of the Alan Turing Institute’s ‘AI for Science’ initiative.

With these facilities generating very large volumes of scientific data, scientists need support and assistance in all aspects of data science. As such, the SciML Group is seeking a number of positions to fulfil these roles, particularly from applicants with a strong background in ML or data science or computational scientists with experience in these areas.

Work Programme:

The successful applicant will be applying and developing novel and state-of-the-art machine learning (ML) and data analysis techniques to analyse large-scale experimental datasets collected with the view of advancing science. These involve, not only applying and developing ML techniques, but also combining multiple datasets, maximising the information extraction, visualising them, and most importantly working with scientists in understanding or characterising fundamental science.  Specific responsibilities include:

• Developing and implementing (e.g., in Python) ML techniques to understand, interpret and extract features and knowledge from experimental datasets,
• Standardise the developed techniques as benchmarks, wherever possible,
• Working with our collaborators, scientists, and hardware vendors for developing scalable ML solutions,
• Assisting in building automated systems underpinned by ML models and relevant statistical models, wherever applicable, and
• Contributing to learning and development activities at STFC, supporting its community by presenting work internally and externally, and by publishing work in peer-reviewed journals and conferences.

Contacts and Communication

• Regular contact with staff internally and externally
• Assist in organising and coordinating technical meetings and relevant project events as appropriate
• Maintain effective engagement with stakeholders.

Personal Skills and Attributes

• Strong communication skills (verbal, written, and presentation)
• Ability to work effectively under pressure, set priorities, and make decisions independently
• Ability to work with uncertain requirements to develop a consensus on a plan of action
• High level of self-motivation and drive
• The ability to work as a team member delivering commitments on time
• A proven track record of analytical and problem-solving skills.

Person Specification:

Both Band D and E will have the following essential and desirable criteria. 

Essential: 

• PhD in computer science or electrical engineering or in a directly relevant area, or PhD in a relevant scientific subject (mathematics, materials science, physics, chemistry or life sciences) or equivalent experience
• Experience in one of the following areas: Bayesian methods, machine learning, signal, or image processing.
• Experience in Python or other scientific programming languages (e.g., C++)
• Experience with one or more machine learning frameworks (e.g., TensorFlow, PyTorch.)
• Awareness of software engineering principles, with regard to robustness, portability, and usability
• Evidence of collaboration
• Evidence of strong scientific communication skills
• Ability to work both as part of a team, and with a high degree of autonomy
• Able to travel in the UK and occasionally abroad

Desirable:

• Experience in high performance computing or in generating/using large-scale systems for ML training
• Familiarity with one or more of the following areas: reinforcement learning, surrogate models, hardware code optimisation, benchmarking, and visualisation.
• Domain expertise in one of the following areas: Materials Physics or Chemistry especially around density functional theory (DFT), Life and Environmental Sciences, Astronomy, or Particle Physics.

In addition to these, applicants for the Band E position must meet the following essential criteria: 

• Deep understanding of managing, structuring and analysing data, including building statistical models and using machine learning technologies. (S&I)
• Strong leadership skills with previous experience of/the ability to lead, motivate and develop others. (S&I)

Further information:

http://www.scd.stfc.ac.uk/ (Scientific Computing Department)

https://www.scd.stfc.ac.uk/Pages/Scientific-Machine-Learning.aspx (Scientific Machine Learning group)

For further information about this position please contact Jeyan Thiyagalingam ([email protected]).

https://www.careersportal.co.uk/UKRI-careers/jobs/data-scientist-senior-data-scientist-2436